We have also explored the conditions to determine which loss function might be helpful in a scenario and compared the performance of all loss functions on four different datasets using Temporal Fusion Transformer[1]} for forecasting. Datasets used in experiments are Electricity Load Diagrams 2011-2014 Dataset[2]}, which has electricity consumption data points of 370 points/clients, PEMS-SF Dataset[3]}, which has 15 months worth of daily data that describes the occupancy rate of different car lanes of the San Francisco bay area freeways across time, Corporación Favorita Dataset[4]} which has Grocery Sales Data and Volatility Dataset[5]} which has daily non-parametric measures of how volatility financial assets or indexes were in the past. The code implementation of experiments is available at https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow
